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7618 2019
April 2019
Kalòs kai agathòs? Government quality and cultural heritage in the regions of Europe Enrico Bertacchini, Federico Revelli
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CESifo Working Paper No. 7618 Category 2: Public Choice
Kalòs kai agathòs? Government quality and cultural heritage
in the regions of Europe
Abstract This paper uses panel data on over 200 regions of Europe to study the spatial distribution of UNESCO sites and the capacity of regional governments to conserve heritage, using new designations in the World Heritage List as a proxy. We test whether the location of a region matters by controlling for the stock of World Heritage in the surrounding regions, and if low regional government quality is an obstacle to inclusion of sites into the List. We find some evidence of within-country regional competition for inscription, and of a positive impact of government quality on the chances of having a UNESCO designation.
JEL-Codes: C230, R100.
Keywords: UNESCO World Heritage, quality of government, regions of Europe, spatial analysis.
Enrico Bertacchini Department of Economics and Statistics
Cognetti de Martiis University of Torino / Italy [email protected]
Federico Revelli Department of Economics and Statistics
Cognetti de Martiis University of Torino / Italy [email protected]
April, 2019
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1 Introduction
The recent decades have seen growing awareness amongst international institutions,
academic circles, and the public at large of the crucial role of good and democratic
governance for economic and social development (UNDP, 2014). The United Nations
system claims to base its actions on the idea of a governance that “promotes equity,
participation, pluralism, transparency, accountability and the rule of law, in a manner
that is effective, efficient and enduring, (...) advances development, by bringing its
energies to bear on such tasks as eradicating poverty, protecting the environment,
ensuring gender equality, and providing for sustainable livelihoods.” (UN, Global
Issues), and the World Bank sees good governance as “the capacity of the government
to effectively formulate and implement sound policies” (World bank, Worldwide
Governance Indicators).
The economic literature has focused on the long-term relationship between the quality
of institutions and economic growth, unveiling their historical role in shaping countries’
and regions’ current economic performance (Acemoglu et al. 2001; Rodrik et al., 2004;
Tabellini 2010). Contemporary government institutions have been also found to affect
the innovative capacity of regions, the effectiveness of their policies, and the return to
investment (Rodriguez-Pose and Di Cataldo, 2015; Rodriguez-Pose and Garcilazo,
2015; Crescenzi et al., 2016).
Despite the growing interest in institutional factors, there has been little research so
far on how government quality affects the effectiveness of public intervention in
complex and multi-dimensional domains such as cultural heritage policy. Cultural
heritage is in fact considered an asset owning public good characteristics (Peacock and
Rizzo, 2008), whose protection requires the design and effective implementation of
appropriate public policies and regulations. For historical reasons, cultural heritage is
particularly relevant in Europe and its conservation and support have become major
policy issues in the last decades. Although the EU has limited powers with respect to
cultural heritage, its relevance has been included in numerous EU Council declarations
and EU funding programs. According to a Eurobarometer special report (European
Commission, 2018), 91% (87%) of European citizens think cultural heritage is important
for their country (region), and 74% of respondents agree that public authorities should
allocate more resources to Europe’s cultural heritage.
The question we address here is if the quality of government at the sub-national level
throughout Europe is an important ingredient for the protection and promotion of
heritage. More specifically, we ask whether the quality of government in the European
regions is a relevant factor in explaining the successful selection of their heritage sites
into the UNESCO World Heritage List, an event that we consider as a proxy of a regional
government’s capacity to effectively protect and promote its heritage sites. In doing so,
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we take for the first time a ‘local’ perspective by combining a novel dataset on UNESCO
World Heritage designations of European regions for the period 2010-2015 with data on
government quality gathered by the Quality of Government Institute at the University of
Gothenburg. Moreover, since we deal with territorial units whose spatial location might
have potentially important implications, we make explicit use of spatial econometric
methods in investigating the determinants of inclusion in the List, in particular by
accounting for potential spill-overs from the existence of already listed sites in the
regions that are adjacent to a candidate site’s region.
In the European context, regional governments play an active role in heritage policy
and in the nomination of sites in the UNESCO World Heritage List. As noted by Rizzo
(2004), sub-central government intervention might in fact be optimal in heritage policy
when the support to cultural heritage is used with the objective of promoting local
economic development (i.e., by enhancing the attractiveness and tourist potential of
heritage sites) or of stimulating the local identity and cohesion of communities. Such an
instrumental approach to link cultural heritage with local development is not limited to
Europe, but has increasingly become a mainstream public policy paradigm in many
industrialized countries (OECD, 2005), suggesting the growing importance of a regional
and local perspective in the design of heritage policies.
Although the proposal to nominate heritage sites for the World Heritage List remains
a prerogative of national governments, the role of local governments in Europe has
become crucial for two main reasons. Firstly, after over 40 years of the UNESCO World
Heritage Convention, most of European countries have already included in the List the
most outstanding heritage sites of national relevance (Frey and Steiner, 2011). New
nominations on the World Heritage List, albeit still proposed for their outstanding
universal value, are thus more likely to be expressions of cultural heritage with greater
local significance. Regional governments might thus be interested in playing a more
active role in the preservation and promotion of their cultural heritage through the World
Heritage List. Secondly, we argue that the changes occurred in the last decade in the
selection process of the UNESCO World Heritage List (UNESCO, 2007), which allow
a state party to submit only up to two complete nominations per year, has increased the
competition between regions in the same country to propose and have their heritage sites
included in the List. As a result, political and institutional differences across regions may
have a dramatic influence on the selection process of heritage sites.
The results of our empirical analysis can be briefly summarized as follows. First,
estimation of a random effects Probit model suggests that the chances of a region having
a heritage site inscribed in the UNESCO list in a given year are positively affected by
the quality of the government of the region, particularly when measured in terms of
ability to curb corrupt behavior and of quality of public services provided. As far as inter-
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regional interdependencies are concerned, we find no significant spill-over on a region
from the stock of World Heritage in surrounding regions. However, the number of
regions in a country turns out to have a significant and negative impact on a region’s
success in site inscription, pointing to within-country competition between regions for
nominating and inscribing sites in the World Heritage List.
The paper is organized as follows: Section 2 presents the local quality of government
indicators; Section 3 discusses the rationale and features of the UNESCO World Heritage
List and its recent trends; in Section 4 we show the spatial distribution of World Heritage
sites across the European regions; in Section 5 we introduce the empirical models and
discuss the results of the econometric analysis; Section 6 concludes.
2 Quality of government
In order to capture the quality of institutions at the regional level, we use the sub-national
Quality of Government (QoG) index proposed by Charron et al. (2014, 2015). Developed
by the Quality of Government Institute at the University of Gothenburg (Sweden), the
index is commonly considered as one of the few sources of data for systematic
comparison of government institutions’ performance across European regions. It is based
on survey data from samples of respondents across countries and regions within the EU
and addresses three main dimensions of government quality, namely public sector
corruption, impartiality and effectiveness in the provision of three public services
(education, healthcare, law enforcement). A region’s QoG index is constructed by
combining the national score of the Worldwide Governance Indicators (Kaufmann et al.,
2009) standardized for the EU sample, with the variation of the QoG index obtained from
the regional survey respect to the country average. The index is available for the sub-
national regions at the NUTS 1 or NUTS 2 level, depending on the country, and is
standardized with a mean of zero and a standard deviation of one, with higher scores
implying higher QoG.
Information on the regional quality of government has been published for three years,
based on subsequent rounds of surveys conducted in 2010, 2013 and 2017. In 2010, 172
NUTS1 and NUTS2 regions in 18 EU countries hosted surveys of about 200 respondents
per region, for a total of 34,000 respondents overall (Charron, 2013). The 2013 index
(Charron et al., 2015) is based instead on a larger survey-based dataset (84,000
respondents) of regions from all EU 28 countries, plus Turkey and Serbia, for a total of
236 political units in 30 countries. Finally, the 2017 survey collects the opinions of
78,000 respondents in 202 regions from 21 European countries (Charron and Lapuente,
2018). The regional scores from the three waves of data are not immediately comparable.
Due to the process of standardization, adding or subtracting units can impact the scores
of other units artificially. However, with each new release, the values of the index from
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the previous years have been retroactively adjusted with validated techniques (see
Charron et al., 2015; Charron and Lapuente, 2018). The regionalisation of the perceived
quality of government institutions unveils interesting patterns that we aim at exploiting
here, notably the very large variance of the index in a number of countries including
Italy, Spain and Portugal, relative to more homogeneous countries like Denmark,
Sweden or the Netherlands.
The QoG index has been used so far to test several research questions, notably how
the quality of regional government institutions affect the innovative capacity of regions
(Rodriguez-Pose and Di Cataldo, 2015), the rates of small and medium-sized enterprises
in the local economy (Nistotskaya et al., 2015), the return of public investments
(Rodriguez-Pose and Garcilazo; Crescenzi et al., 2016) and the regional attractiveness to
migrants (Ketterer and Rodríguez-Pose, 2015).
Although it is based on perception of local government institutions in three specific
areas of public service provision (health care, education, and law enforcement), we
contend that the QoG index can be extended to assess the effectiveness of heritage
policies at the local level as well. Protection and promotion of heritage can be considered
in many respects as a form of government’s provision of public goods and services
(Throsby, 2010), for the quality of political decision-making and policy implementation
influence the outcome of public intervention in this domain. Furthermore, as the regional
quality of government is positively associated with higher levels of social trust (Charron
et al., 2014), it is very likely that more cohesive communities exhibit stronger
preferences for the conservation and support to their heritage assets, being expressions
of their local identities.
3 The UNESCO World Heritage list
The World Heritage List is the main implementing mechanism of the 1972 UNESCO
World Heritage Convention, an international agreement that seeks to encourage the
identification, protection and preservation of cultural and natural heritage around the
world that is considered to be of outstanding value to humanity. Inclusion of cultural and
natural sites on the World Heritage List is the result of a selection process that occurs
during the annual World Heritage Committee sessions. State parties propose, among the
national properties previously included in a Tentative List, 1 heritage sites to be
nominated in the final List, and these are included if they meet at least one of the 10
criteria defining the outstanding universal value and, additionally, the conditions of
uniqueness, authenticity, and integrity (UNESCO, 2017).
1 National Tentative Lists are inventories of sites that State Parties submit for possible inscription in the World
Heritage List and thus represent the first act of initiative as well as a necessary condition for obtaining the World
Heritage designation.
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The World Heritage List has become increasingly popular and many have regarded it
as the most effective international legal instrument for the protection of cultural and
natural heritage (Titchen 1996; Strasser 2002). As of 2016, some 191 countries have
ratified the Convention and a total of 1,031 properties have been included on the World
Heritage List. In line with the principles of the Convention, the inclusion of a site in the
World Heritage List signals the quality of the property and the government’s
commitment towards the international community for its protection and conservation.
As the World Heritage designation does not directly guarantee greater protection or
access to financial resources from UNESCO, to ensure the integrity of the selected sites
governments have thus to comply through their heritage policies and interventions with
international recognized standards. Yet, given the popularity of the List, obtaining a
UNESCO World Heritage designation has become also highly desirable as it might
attract the attention of donors and for-profit firms, or it can be used to market the sites
as tourist destinations (Harrison and Hitchcock 2005; Fyall and Rakic 2006 ).
The List has been long recognized to be unbalanced in the geographical areas of the
world that are represented, with the majority of World Heritage being cultural sites
located in developed regions, in particular in Europe and North America (UNESCO
2007). While some commentators have stressed as a cause of this unbalance the Western-
centered conceptualization of tangible heritage in the design of selection criteria
(Musitelli 2002), a growing empirical literature has unveiled political and economic
determinants that may have influenced the selection process and led to unbalances in the
World Heritage List (Bertacchini and Saccone 2012; Frey et al. 2013; Parenti and De
Simone 2015, Bertacchini et al. 2016). Significantly, factors such as a country’s income
level, economic power, tourism specialization and active involvement on the World
Heritage Committee sessions have been found to have an impact on the composition of
the World Heritage List.
In order to rectify some of the representativeness gaps, since 2002 new measures to
achieve a balanced representation have limited both the nomination capacity of states
and the number of proposals examined at yearly Committee sessions. Currently, states
can submit up to two complete nominations per year, provided that at least one is a
natural heritage site, and the Committee can review up to forty-five nominations each
year, inclusive of nominations deferred and referred by previous sessions.
4 The distribution of World Heritage sites across the EU regions
Studies and statistics concerning UNESCO World Heritage have usually focused on the
distribution of sites at the country level, as state parties are the key actors within the
UNESCO World Heritage Convention. Conversely, there has been little attention to a
regional perspective on UNESCO World Heritage and in particular to the distribution of
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World Heritage sites across the European regions. Europe is the area hosting the highest
share of World Heritage properties, with some countries, namely France, Italy and Spain,
ranking at the top for the number of sites in the World Heritage List. A regional
perspective on European World Heritage can thus be useful to verify in a more fine-
grained way whether geographical imbalances in the List noticed at the global level and
between-countries do occur across European regions too. Further, through this approach
it is possible to add insights into the spatial dependence of regions as to the localization
of World Heritage sites, overcoming national boundaries.
To obtain the number of World Heritage sites in each region we use the information
about heritage properties drawn from the UNESCO World Heritage Center Database
(Source: http://whc.unesco.org/en/list). The database provides unique GIS coordinates
that we use to identify the region (NUTS2 level) in which the heritage property is located.
In cases where the UNESCO site area extends over multiple regions, the site is assigned
to the region according to the official coordinates.2
As for trans-boundary sites (those sites that are recorded in the List as belonging to
different countries), we adopt the following approach. For those sites that are located at
the border of neighboring regions in two or more countries, we assign them equally to
all involved regions. Conversely, we exclude transnational serial properties that are
extremely scattered across countries and regions because it is difficult to identify the
leading region in the World Heritage nomination process. 3 We do not make any
distinction between cultural, natural and mixed properties as defined by UNESCO World
Heritage Convention. Differentiating between cultural and natural sites would create
difficulties in the European context due to the very low number of natural sites.4
UNESCO sites are spread in a relatively homogeneous way through the European
continent, with 63.5% of the regions having at least one property included on the List.
However, the regions scoring the highest number of sites tend to be located in the
Mediterranean area and, in particular, in countries like Italy and Spain that exhibit also
the highest number of sites on the List. On the other hand, regions without World
Heritage sites are more likely to be found in the United Kingdom and in Central and
Eastern Europe.
Figure 1 displays the distribution of World Heritage sites in 2015 across European
regions (NUTS2), providing a first illustration of the most visible spatial patterns.
2 To give an example, the World Heritage site of the Dolomiti in Italy, that extends through both the Veneto and the
Trentino Alto Adige regions, has been assigned to Veneto based on the reported GIS coordinates 3 Transnational serial properties are those where two or more spatially distinct components stretch across two or more
neighbouring countries, as individual components if they create a thematic, functional, historic, stylistic or typological
series with other, spatially distinct components. 4 Natural sites are only about 10% of listed properties in Europe compared to about 20% on the whole World Heritage
List.
8
Figure 1: Number of UNESCO World Heritage Sites per region (NUTS2), 2015
In order to detect spatial dependence of World Heritage sites across the European
regions, we first use a global Moran’s I statistic as a measure of association, and then
local indicators of spatial association (LISA). LISA provide insights at the local level by
showing the tendency of observed phenomena to locate or not in neighboring regions
and are computed through a local Moran’s statistic where the population is a group of
neighboring regions depending on a contiguity criterion (Anselin, 1995).
Figure 2 presents the two measures using the first-order contiguity neighborhood
criterion, where the set of neighbors of region i includes all regions sharing a border with
it, and where each neighboring region j is attributed the same weight.5
5 Because the Queen contiguity weight matrix drops from the analysis 17 neighborless regions (i.e. islands), we also
tested a 5-nearest neighbors weight matrix as an alternative approach, obtaining very similar results.
9
Figure 2: Global and Local indicators of spatial association for World Heritage sites
(First order Queen contiguity weight matrix)
(a) Global Moran’s I, p-value = 0.001 (b) LISA Map, significant clusters
The spatial association of UNESCO World Heritage sites across European regions
measured by the Global Moran’s I statistic is positive (0.16), but it is not particularly
high.6 Looking at the Moran scatterplot (Figure 2(a)), this is due to a relatively large
number of regions in the upper left and lower right quadrants, which indicate spatial
clustering of observations with diverging values. In other words, while some European
regions with many (few or none) World heritage sites do tend to cluster in space, there
are parts of the continent where regions with many (few) sites are surrounded by
neighboring regions with few (many) sites. The map of local indicator of spatial
association (LISA) displayed in Figure 2(b) highlights the most relevant local patterns
of concentration between regions. Even if a large part of reported local Moran’s I
statistics are not significant, a look at both the high-high (red) and low-low (blue) clusters
confirms the descriptive finding identified in Figure 1. Regions with high number of
World Heritage sites tend to be located in Southern European countries, whereas clusters
of regions scoring low values of heritage sites are more likely to be located in Central
and Eastern Europe (including Turkey).
6 A deeper inspection of the data indicates a Global Moran’s I statistics of 0.23 when considering only the regions in
Western European countries (Portugal, Spain, France, UK, Ireland, Italy, Germany and Benelux), while no spatial
association (0.07) for the regions in Eastern and Northern Europe.
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To explain the geographical distribution of World Heritage Sites across the European
Regions, Table 1 presents the estimated coefficients from a cross-sectional linear
regressions including as covariates geographical and historical factors that may have
determined the potential of a region to obtain a World Heritage deignation.7 The size of
the regions is a first rough indicator for the potential of having heritage sites included in
the List, assuming that the larger is a region, the higher is the likelihood to host within
its borders some outstanding heritage worth to be nominated in the UNESCO List. As
expected, the coefficient for this variable is always positive and significant (varying
between 5% and 10% significance level, depending on the specification). More
interestingly, the number of World Heritage sites is significantly explained by proxies of
the cultural potential of the region, based on its historical development. In particular,
using Chandler and Fox’s data on the geographical evolution of major urban settlements
in history (Chandler and Fox, 2013), we construct variables for different historical
periods (XI, XVI and XVIII century) reflecting the number of the most populated cities
in Europe (top one hundred) located in each region. From an historical perspective, major
urban centers have been the loci of the most intense socio-economic activities as well as
of the highest achievements in cultural and artistic expression. As a result, one can expect
that the more a region has hosted major urban centers during the past, the more World
Heritage sites it contains today. As can be noted in Regressions 1-3 in Table 1, the
coefficients indicating the effect of the number of major cities in European history on
the number of current sites is positive and highly significant, with the distribution of
major cities across regions in the XVI century leading to the largest effect on the number
of World Heritage sites. This result also points out how most of European World
Heritage can be traced back to this specific historical period. The effect of the historical
cultural potential of regions also holds when we use a cumulative variable based on the
previous three periods (regression 4).
7 Following Frey et al. (2013) we have also estimated count data models considering that the dependent variable can
only take natural numbers. The results and significance of coefficients are similar in the two settings and for clarity
and convenience in interpreting the results we opted for the OLS ones in this case.
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Table 1: Determinants of the total number of sites in the World Heritage List 2015
(1) (2) (3) (4)
SIZEmil 0.00910* 0.00838** 0.0111** 0.00881**
(0.00451) (0.00407) (0.00542) (0.00411)
XIcen 0.730***
(0.192)
XVIcen 0.811***
(0.182)
XVIIIcen 0.687***
(0.180)
TotHistUrb 0.303***
(0.0674)
Constant 0.842*** 0.833*** 0.836*** 0.796***
(0.120) (0.129) (0.122) (0.126)
Observations 289 289 289 289
R-squared 0.220 0.242 0.184 0.252
Adjusted R-squared 0.215 0.237 0.179 0.247
Standard errors (clustered at country level) in parentheses: *** p<0.01, ** p<0.05, * p<0.1.
As a final piece of evidence, figure 3 presents the distribution of new sites included
in the World Heritage List over the period 2010-2015, based on the regional allocation
method previously described. With only 25 new listed properties,8 the relatively low
number of inscriptions is mainly the effect of the rules and procedures adopted in the last
decade by UNESCO, which have restricted to one (or two in special cases) the number
of nominations that can be submitted by state parties for selection. As a result, as shown
in figure 3, very few regions have obtained a UNESCO designation in the reference
period, with the exception of only two (Sicily in Italy and Izmir in Turkey) with two new
listed sites. Interestingly, the regions that have been able to include new sites in the List
tend to be relatively clustered in some specific countries (i.e., France, Germany,
Portugal, Italy, and Turkey) suggesting that regions’ behavior may still be influenced to
some extent by group or country factors.
8 The number of new sites considered here for the period 2010-2015 is lower than the actual number of
inscriptions as we excluded transnational serial sites for methodological reasons.
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Figure 3: Regions with new sites inscribed in the World Heritage List, 2010-2015
5 Econometric analysis
5.1 Empirical strategy and variables
To investigate the determinants of the probability of European regions having a site
included in the UNESCO World Heritage List we focus on two main channels. First, we
consider the regional quality of government as a factor affecting the level of protection
and support to heritage and, consequently, the ability of a region to obtain the World
Heritage designation. Secondly, we test whether the location of a region matters for the
chances of its nominations to obtain the World Heritage designation by controlling for
the stock of listed heritage sites that are located in the surrounding regions. In fact, the
documented process of spatial concentration of world heritage sites might indicate, after
controlling for the quality and stock of the heritage endowment, potential spill-overs
across regions in heritage policy and their ability to obtain World Heritage designations.
We use a panel data set of European regions r=1,...,R over six years t=2010,...,2015.
The key variable that we observe at the regional level is a binary variable, 𝑖𝑟𝑡, equaling
1 if a region r has (at least) one new site inscribed into the UNESCO list in a given year
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t. 𝑖𝑟𝑡 depends in turn on the realization of an underlying (unobserved) score, 𝑖𝑟𝑡∗ , with
𝑖𝑟𝑡 = 1 (0) if 𝑖𝑟𝑡∗ 0 (< 0). The 𝑖𝑟𝑡
∗ score is modelled in equation (1):
𝑖𝑟𝑡∗ = 𝒙𝑟𝑡
′ 𝛽 + 𝜃𝑄𝑜𝐺𝑟�̃� + 𝛾𝑊𝐻𝑟𝑡−1 + 𝛿 ∑ 𝜔𝑟𝑗𝑗 𝑊𝐻𝑗𝑡−1 + 𝜏𝑇𝑟𝑡−2 + 𝑦𝑡 + 휀𝑟𝑡 (1)
𝒙𝑟𝑡′ in equation (1) is a vector of regional variables at year t, namely (logarithm
transformations of) population, size and income per capita. These covariates have been
commonly considered as determinants of countries’ ability to inscribe heritage sites in
the UNESCO World Heritage List, and we include them to test their effect at the sub-
national level.
The Quality of Government index 𝑄𝑜𝐺𝑟�̃� is observed in 2010 and 2013, and:
�̃�=2010 if t≤2012 and �̃�=2013 if t≥2013. 𝑊𝐻𝑟𝑡−1 and 𝑊𝐻𝑗𝑡−1 index the stock of
World Heritage sites in regions r and j at time t−1, respectively. 𝜔𝑟𝑗 ∈ (0,1), r,j=1,...,R,
is an (R×R) set of spatial weights, with 𝜔𝑟𝑗 = 1 if regions r and j are adjacent (i.e., they
share a common border), 0 otherwise, so that 𝛿 captures the impact of the total number
of listed sites in the neighborhood at the end of the previous period on the probability of
having a site listed in a region in period t. 𝑇𝑟𝑡−2 is the number of properties in the
UNESCO Tentative List in region r at time t−2. As the Tentative List is made of sites
which state parties consider to be of outstanding universal value and suitable for
inscription on the World Heritage List, this variable is a proxy of the quality and quantity
of regions’ total heritage endowment not yet included in the List. The two years lag has
been chosen in this case, because, according to UNESCO operational guidelines, state
parties must submit sites to the Tentative List at least one year prior to the submission of
any nomination.
The inclusion of the lagged stock of sites in the World Heritage List deserves some
further consideration for potential endogeneity concerns. In fact, one could argue that
the same factors that we claim currently influence the new inscriptions of World Heritage
sites at the regional level may have equally affected the ability to include sites in the
World Heritage and Tentative List by regions in the past. However, it is worth to remind
that the stock of World Heritage sites in each region is the result of a cumulative process
of inscriptions in the List over more than 40 years, a period during which regions have
played a minor role in the nomination and selection process relatively to the period we
are analyzing here.
As for the Quality of Government index at the regional level (𝑄𝑜𝐺𝑟�̃�), we consider
both the global index and its sub components, expressing the quality of public services,
impartiality in provision, and control of corruption. We use the values observed in 2010
and 2013 and reported in the dataset released with the 2013 wave, which excludes the
observations for the Turkish regions. Previous works (Rodriguez-Pose and Di Cataldo,
14
2015; Rodriguez-Pose and Garcilazo, 2015; Crescenzi et al., 2016) have adopted the
regional quality of government in panel settings, but used only the value observed in one
wave. As a result, by using two waves, our approach can capture the within-country
evolution of regions’ quality of government during the period of analysis.
While we seek to isolate the determinants of the conservation of heritage at the
regional level and the ability to include heritage in the World Heritage List, we cannot
completely rule out that this outcome can be influenced by some country-level
characteristics that may enable or hinder regions’ activity depending on the procedures
of the UNESCO World Heritage nomination and selection process. As a result, we
include in our specification the number of regions of a country to capture the effect of
the competition between regions in the same country. Further, we consider two
additional dimensions of the activity of a country in the World Heritage system that may
affect the actual capacity of its regions to inscribe sites in the List. The variable
Committee is a binary variable indicating whether or not a country is serving on the
World Heritage Committee. Previous works (Bertacchini and Saccone 2012; Frey et al.
2013) suggest that this variable has a positive impact on the inscription of World
Heritage sites, as countries that serve the World Heritage Committee in a given year
might be more likely to propose and inscribe sites, thus generating a greater chance for
the regions within their borders. Finally, we include the total number of years a country
has been member of the World Heritage Committee. This variable captures differences
in the active involvement of countries in the UNESCO World Heritage system, and may
signal the interest of national and local governments in using the World Heritage List as
a mechanism for the protection and promotion of their heritage. Table A1 in the
Appendix presents the summary statistics of all variables used. We estimate equation (1)
by Probit with random effects.9
5.2 Results
Table 2 summarizes the results obtained by estimating equation (1) under different
specifications. First, we consider the spatial effect of the stock of World Heritage sites
in neighboring regions on the likelihood of obtaining a new inscription (regressions 5
and 6); next, we include the regional quality of government scores (regressions 7 and 8);
finally, we estimate the full model with both effects (regressions 9 and 10). In all
instances, we show the results when taking (or not) into account year fixed effects that
pick common time factors, i.e., the specific World Heritage Committee behavior and
9 A random effects specification is preferable to a fixed effects one in this context because of significant
between-group variation in the explanatory variables and little within-group variation (due to the rare
occurrence of non-zero outcomes) in the dependent variable.
15
attitude towards selection of properties to be included in the List in a given annual
session.
Table 2: Random-effects panel Probit estimation of having a new site included in the
World Heritage List
(5) (6) (7) (8) (9) (10)
WH -0.0238 -0.0252 0.0169 0.0147 0.0171 0.0149
(0.0637) (0.0612) (0.0664) (0.0642) (0.0676) (0.0655)
Sites Tentative List 0.107** 0.110** 0.109* 0.113** 0.109* 0.113*
(0.0502) (0.0507) (0.0567) (0.0578) (0.0574) (0.0583)
Neighboring WH -0.00468 -0.00398 -0.00449 -0.00391
(0.0218) (0.0216) (0.0253) (0.0251)
QoG 0.339*** 0.349*** 0.336*** 0.346***
(0.116) (0.116) (0.120) (0.119)
Population (ln) 0.132 0.142 0.204* 0.218* 0.204* 0.218*
(0.104) (0.103) (0.118) (0.117) (0.117) (0.116)
Size (ln) 0.0555 0.0497 -0.0284 -0.0326 -0.0210 -0.0261
(0.0753) (0.0743) (0.0848) (0.0845) (0.0879) (0.0870)
Income p.c. (ln) 0.482** 0.446** -0.0388 -0.0862 -0.0286 -0.0770
(0.207) (0.200) (0.313) (0.312) (0.316) (0.313)
Num. Regions -0.00960 -0.00907 -0.0149* -0.0145* -0.0149* -0.0146*
(0.00766) (0.00766) (0.00825) (0.00824) (0.00821) (0.00820)
Years in WH Comm. 0.0320*** 0.0312*** 0.0442*** 0.0435*** 0.0450*** 0.0441***
(0.0119) (0.0114) (0.0111) (0.0104) (0.0126) (0.0120)
WH Committee -0.0308 -0.0340 -0.129 -0.133 -0.135 -0.138
(0.200) (0.195) (0.206) (0.201) (0.212) (0.207)
Constant -9.621*** -9.639*** -4.768 -4.762 -4.917 -4.892
(2.658) (2.506) (3.348) (3.266) (3.445) (3.344)
Year Dummies NO YES NO YES NO YES
Observations 1,664 1,664 1,606 1,606 1,606 1,606
Number of regions 279 279 269 269 269 269
Log Pseudo-
likelihood
-126.5 -125.4 -119.1 -118 -119.1 -118
Note: Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1.
Considering the stock of World Heritage sites in the neighboring regions first
(Neighboring WH), the coefficient for this variable is never significant under any
specification. Thus we can reject the hypothesis that a spatial spill-over impacts the
probability to obtain new inscriptions at the regional level. Conversely, the coefficient
on the regional quality of government (QoG) is estimated to be positive and always
significant at the 5% level (reg. 7-10) indicating that local governments that are more
accountable are also more likely to protect and support their cultural heritage, leading to
a higher chance to obtain the UNESCO World Heritage recognition. Holding all other
variables to their means, one unit increase in the quality of government (corresponding
to one standard deviation) raises the probability by one region of obtaining a World
16
Heritage designation in one year by one percentage point. To have a clearer picture of
the size of the effect for this variable, Figure 4 displays the predicted probability of
inscribing in one year a new site according to variation in the regional quality of
government. Regions with a quality of government score equal to 1 (e.g., Vlaams Gevest
region in Belgium) exhibit a 3% probability of having a new heritage site included in the
World Heritage List during any year, which is five times larger than the corresponding
chances of regions where the quality of government score is equal to -1 (e.g., Abruzzo
region in Italy).
Figure 4: Predictive probabilities of obtaining a World Heritage Site according to
variation in quality of government
As one could expect, a region’s capacity to nominate and inscribe site in the World
Heritage List is positively affected by the number of sites in the Tentative List. This
effect is stable over regressions, even if only at the 10% significance level. At the same
time, the results point out that a number of circumstances at the country level play a role.
In particular, while being member of the World Heritage Committee by a country does
not lead to a significant effect at the regional level, the coefficient of the number of years
a country has served to the Committee is positive and highly significant. This finding
suggests that the likelihood regional governments have to protect and promote the
cultural heritage through the UNESCO List varies based on the historical involvement
in the World Heritage system by their country. Additionally, the number of regions in a
country turns out significant at the 10% level and with a negative coefficient, indicating
17
some effect of within-country competition between regions for nominating and
inscribing World Heritage sites.
Among the other regional variables, the coefficient of population is statistically
significant and with a positive sign. One possible explanation of this result is that more
populated European regions tend to have historically more urbanized areas and this may
reflects into a larger stock of available cultural heritage. Moreover, the effect of regional
income per capita is significant only when the quality of government is not included in
the model (reg. 5-6) suggesting that the latter factor is a more robust predictor of regions’
capacity in protecting heritage and inscribing sites in the World Heritage List.
The decomposition of the Quality of Government index into its three basic
components in Table 3 (Regressions 11-13) displays interesting differences in the link
between specific institutional factors and the capacity to inscribe World Heritage Sites
at the regional level. In all three cases, the main results obtained in previous
specifications hold, with the coefficients for the three sub-indexes being positive and
significantly different from zero. Interestingly, the component referring to the control of
corruption exhibits the highest significance and largest coefficient value among the three
sub-indexes, while government impartiality has the lowest significance and smallest
effect. This finding is in line with previous research showing that the level of perceived
corruption has the strongest and most significant effect on regional performance in
various domains, such as innovation capacity (Rodriguez-Pose and Di Cataldo, 2015)
and presence of small and medium-sized enterprises (Nistotskaya et al., 2014). Similarly,
since the conservation of cultural heritage strongly relies on the enforcement of
regulations and investment in capital assets, the corruption dimension, rather than the
quality and impartiality in the provision of public services, is possibly the one that better
captures the capacity of regional governments in heritage policy-making and
enforcement.
Considering we find no spatial effect for the stock of World Heritage in neighboring
regions on the likelihood of inscription of World Heritage sites, in regression 14 we
additionally test whether spatial spillovers might occur only within country, due to
national institutional factors which might hinder the effect across national borders. For
this reason, as alternative specification we employ a spatial weighting matrix that
accounts only for within-country adjacent regions. In this case too, though, the stock of
World Heritage sites in adjacent regions does not turn out to be a significant predictor,
while the other effects hold.
18
Table 3: Random-effects panel Probit estimation
(11) (12) (13) (14)
WH 0.00905 -0.00420 0.0155 0.0184
(0.0636) (0.0664) (0.0707) (0.0667)
Sites Tentative List 0.113* 0.112* 0.125* 0.111*
(0.0601) (0.0577) (0.0637) (0.0605)
Neighboring WH -0.00876 -0.00520 -0.000241
(0.0247) (0.0232) (0.0267)
Neighboring WH (within
country)
-0.0257
(0.0268)
QoG 0.336***
(0.117)
Quality public services 0.302**
(0.125)
Impartiality 0.225**
(0.107)
Control of corruption 0.433***
(0.121)
Population (ln) 0.209* 0.179 0.230** 0.215*
(0.112) (0.116) (0.116) (0.113)
Size (ln) 0.00805 0.0167 -0.0378 -0.00596
(0.0885) (0.0889) (0.0919) (0.0818)
Income per capita (ln) 0.0237 0.180 -0.196 -0.0229
(0.300) (0.288) (0.319) (0.298)
Num. Regions -0.0127 -0.0144* -0.0149* -0.0140*
(0.00789) (0.00863) (0.00806) (0.00807)
Years in WH Committee 0.0410*** 0.0402*** 0.0459*** 0.0476***
(0.0115) (0.0121) (0.0127) (0.0118)
WH Committee -0.145 -0.103 -0.154 -0.170
(0.209) (0.207) (0.205) (0.208)
Constant -6.044* -7.215** -3.824 -5.537*
(3.227) (3.120) (3.493) (3.228)
Year Dummies YES YES YES YES
Observations 1,600 1,600 1,600 1,606
Number of regions 267 267 267 269
Log Pseudo-likelihood -118.3 -119.2 -116 -117.5
Note: Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1.
One peculiar characteristic of World Heritage designation is that the preparation of
the nomination until the successful inclusion in the List is a lengthy process that might
take several years. One concern in analyzing new World Heritage inscriptions by regions
is that a year-by-year perspective within a panel data approach might be too fine-grained
to account for the World Heritage designation process. For example, if a region is able
to inscribe a heritage site at year t, the probability of obtaining a new World Heritage
designation at year t+1 is possibly lower and conditioned on the previous outcome. To
check the robustness of our results against this argument, Table 4 provides estimations
using a collapsed dataset to a single period. In this case, we estimate the probability that
19
European regions obtain at least one heritage site in the period 2010-2015 by exploiting
the cross-sectional variance of our explanatory variables.
Table 4: Probit on collapsed dataset and Linear Probability Spatial Error Model (SEM)
(15) (16) (17) (18) (19)
Probit Probit Probit Probit LP SEM
WH 0.0267 0.0116 -0.00978 0.0123 -0.0026
(0.0937) (0.0896) (0.0898) (0.0974) (0.014)
Sites Tentative List 0.192** 0.201* 0.195** 0.224** 0.040***
(0.0979) (0.103) (0.0991) (0.102) (0.014)
Neighboring WH 0.0114 0.00357 0.00752 0.0166 0.0002
(0.0451) (0.0457) (0.0426) (0.0458) (0.004)
QoG 0.623** 0.061**
(0.255) (0.029)
Quality public services 0.578***
(0.221)
Impartiality 0.425*
(0.223)
Control of corruption 0.764***
(0.267)
Population (ln) 0.324* 0.313* 0.267 0.336* 0.0435
(0.174) (0.168) (0.169) (0.176) (0.027)
Size (ln) -0.117 -0.0578 -0.0404 -0.105 -0.003
(0.103) (0.0846) (0.0876) (0.0969) (0.021)
Income per capita (ln) -0.179 -0.0267 0.260 -0.285 0.0028
(0.480) (0.390) (0.395) (0.520) (0.068)
Num. Regions -0.0258* -0.0249* -0.0282* -0.0253* -0.0025
(0.0147) (0.0142) (0.0148) (0.0143) (0.002)
Years in WH Committee 0.0645*** 0.0570*** 0.0551** 0.0699*** 0.008***
(0.0234) (0.0217) (0.0219) (0.0259) (0.003)
WH Committee 0.0604 0.0785 0.0952 0.0385 0.0094
(0.0891) (0.0848) (0.0850) (0.0886) (0.014)
Lambda 0.170**
(0.081)
Constant -3.735 -5.561 -7.814* -3.086 -0.592
(4.589) (4.028) (4.084) (4.854) (0.759)
Observations 267 265 265 265 267
Log Pseudo-likelihood -66.70 -66.28 -67.51 -64.35 -30.22
R-Squared 0.140
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1.
20
Regressions 15-18 show Probit estimation with robust standard errors using the global
index of the regional quality of government and its components. The main results hold
in this specification too, with similar level of significance for the coefficients and,
expectedly, higher marginal effects. As shown in Figure 5, differences in the regional
quality of government scores have a large impact on the success of regional heritage
policy: regions registering a regional QoG score of 1 have an 18% chance to inscribe at
least one World Heritage site during the period, relatively to a 3% probability of regions
scoring -1.
Figure 5: Predictive probabilities of obtaining a World Heritage Site according to
variation in quality of government, 6 years period
Finally, in regression 19 of Table 4 we estimate a spatial error model (SEM), that is, a
linear regression model that allows for a first-order spatial auto-regressive process in the
residuals to test the robustness of our results against unobserved neighboring effects. The
results are in line with those obtained with the Probit model, and the coefficient on the
spatially correlated errors (lambda) is positive and significant, confirming the presence
of spatially correlated unobserved determinants of the chances of a region having its sites
included in the World Heritage list.
6 Concluding Remarks
This paper has used a newly constructed panel dataset that matches the distribution of
UNESCO world heritage sites across over 200 European regions with indicators of
quality of government to test whether the characteristics of governments in terms of
probity, fairness and ability to provide public services positively affect regions’ capacity
21
to protect and support their heritage, and proxied that capacity by the chances to have
heritage sites nominated and included in the UNESCO World Heritage list. The paper
contributes to the literature on the political and economic determinants of UNESCO
World Heritage by adding a regional and spatial perspective to the analysis. Further, it
contributes to the scholarly debate on the effects of the quality of institutions and
governance by providing novel insights in the field of heritage and cultural policies.
Knowledge of the geographical distribution of sites across the European regions has
allowed us to give a fresh picture of the spatial pattern of the existing stock of UNESCO
sites in Europe as well as to test for the existence of spill-overs from the presence of
heritage sites in a region to the chances of new sites being inscribed in neighboring
regions. After controlling for regional and national factors previously used to explain the
nomination and inscription activity of World Heritage sites, the empirical analysis
unveils that the quality of the regional governments positively influences the chances of
a region having a heritage site inscribed in the UNESCO list in a given year. Conversely,
we find no significant spill-over impact across regions on the ability to obtain World
Heritage designations based on the stock of world heritage sites in neighboring regions.
The results of the analysis have relevant policy implications too. They empirically
confirm that the effective protection and support to cultural heritage is influenced not
only by national heritage policies, but also by indicators of government ‘health’ at the
sub-national level that can be considered as proxies of a more general commitment of a
local community to protect and promote its heritage. In particular, high levels of
corruption emerge as the main obstacle to effective heritage protection policy in the
regions of Europe. Additionally, as for the UNESCO World Heritage selection process,
the findings suggest that, at least in the European context, the rules adopted to limit the
annual number of national sites to be included in the World Heritage List might have
partly shifted the competition from states to regions within the same country, making the
accountability of regional governments an increasingly relevant factor for obtaining new
designations.
22
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25
APPENDIX
Table A1: Summary Statistics
VARIABLES N mean sd min max
New Sites (Dependent variable) 1,890 0.0180 0.133 0 1
WH (lag) 1,890 1.108 1.292 0 7
Neighboring WH (lag) 1,890 4.903 4.222 0 24
Site Tentative List 1,890 0.733 1.312 0 9
Number of Regions 1,920 20.42 12.80 1 40
Quality of Government 1,608 0.141 0.957 -2.838 2.639
- Quality public services 1,602 0.142 0.949 -3.273 2.797
- Impartiality 1,602 0.130 0.956 -3.241 2.670
- Control of corruption 1,602 0.124 0.960 -2.836 2.240
World Heritage Committee 1,914 0.219 0.414 0 1
Years World Heritage Committee 1,914 8.617 7.099 0 23
Population (ln) 1,910 14.13 0.848 10.23 16.48
Size (ln) 1,920 9.163 1.330 2.565 12.33
Income per capita (ln) 1,696 10.09 0.394 8.854 12.07